#data-pipeline

#data-pipeline

In large-scale data ingestion systems, small architecture choices can have dramatic performance implications.

Dynamic column transformations enable us to define rules within the schema, allowing Spark jobs to adapt without hardcoding changes, simplifying the data pipeline process.

Date and time operations are essential in retail, finance, logistics, and streaming applications where trends, seasonality, and recency are critical.

Cloud pricing can be surprisingly complex, and with nearly 5 million SKUs across major cloud providers, maintaining accurate cost estimates is essential.

In any modern digital business, data is king, forming the core of decision-making processes and driving essential metrics, like billable usage for invoicing.